Robot adaptive behavior to suit patient needs and enable more intensive rehabilitation tasks

The last advances and researches have shown that intensive task-oriented therapy is highly effective for improving the arm function of individuals after stroke, brain injury or other neurological and motor diseases and injuries. This paper studies different control and trajectory planning techniques used on human machine interaction, in order to make a robot behave in a more human compliant way. This work presents a novel method that let the system change its apparent dynamic parameters, by gathering and processing several physiological data online at rehabilitation time. This allows the robot to adapt to different patients and situations, maintaining the therapy as intensive as possible without compromising patients health or let the individual get stressed which would result in a decay of the task performance and loss of motivation.

[1]  M A Lemay,et al.  Issues in impedance selection and input devices for multijoint powered orthotics. , 1998, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[2]  William S. Harwin,et al.  Minimum jerk trajectory control for rehabilitation and haptic applications , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[3]  Tawiwat Veeraklaew,et al.  A Study on the Comparison between Minimum Jerk and Minimum Energy of Dynamic Systems , 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

[4]  D. Wade,et al.  Enhanced physical therapy improves recovery of arm function after stroke. A randomised controlled trial. , 1992, Journal of neurology, neurosurgery, and psychiatry.

[5]  T. Platz [Evidence-based arm rehabilitation--a systematic review of the literature]. , 2003, Der Nervenarzt.

[6]  Robert Riener,et al.  Robot-aided neurorehabilitation of the upper extremities , 2005, Medical and Biological Engineering and Computing.

[7]  Neville Hogan,et al.  Impedance Control: An Approach to Manipulation: Part I—Theory , 1985 .

[8]  Peter Langhorne,et al.  Effects of Augmented Exercise Therapy Time After Stroke: A Meta-Analysis , 2004, Stroke.

[9]  T. Flash,et al.  The coordination of arm movements: an experimentally confirmed mathematical model , 1985, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[10]  Neville Hogan,et al.  Impedance Control: An Approach to Manipulation , 1984, 1984 American Control Conference.

[11]  G. Kwakkel,et al.  Effects of intensity of rehabilitation after stroke. A research synthesis. , 1997, Stroke.

[12]  T. Flash,et al.  Arm Trajectory Modifications During Reaching Towards Visual Targets , 1991, Journal of Cognitive Neuroscience.

[13]  D.J. Reinkensmeyer,et al.  Automating Arm Movement Training Following Severe Stroke: Functional Exercises With Quantitative Feedback in a Gravity-Reduced Environment , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[14]  J. Krakauer Motor learning: its relevance to stroke recovery and neurorehabilitation. , 2006, Current opinion in neurology.

[15]  Hermano Igo Krebs,et al.  MIT-MANUS: a workstation for manual therapy and training. I , 1992, [1992] Proceedings IEEE International Workshop on Robot and Human Communication.

[16]  M. Levin,et al.  Improvement of Arm Movement Patterns and Endpoint Control Depends on Type of Feedback During Practice in Stroke Survivors , 2007, Neurorehabilitation and neural repair.

[17]  Leslie G. Ungerleider,et al.  The acquisition of skilled motor performance: fast and slow experience-driven changes in primary motor cortex. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[18]  Monica Cameirao,et al.  Physiological Responses during Performance within a Virtual Scenario for the Rehabilitation of Motor Deficits , 2007 .